Inpatient Flow Distribution Patterns at Shanghai Hospitals

Abstract

Empirical studies based on patient flow data are needed to provide more materials to summarize the general pattern of patient distribution models. This study takes Shanghai as an example and tries to demonstrate the inpatient flow distribution model for different levels and specialties of medical institutions. Power, negative exponential, Gaussian, and log-logistic models were used to fit the distributions of inpatients, and a model of inpatient distribution patterns in Shanghai was derived, based on these four models. Then, the adjusted coefficient of determination (R2) and Akaike information criterion (AIC) values were used to assess the model fitting effect. The log-logistic function model has a good simulation effect and the strongest applicability in most hospitals. The estimated value of the distance-decay parameter β in the log-logistic function model is 1.67 for all patients, 1.89 for regional hospital inpatients, 1.40 for tertiary hospital inpatients, 1.64 for traditional Chinese medicine hospital inpatients, and 0.85 for mental hospital inpatients. However, the simulations at the tumor, children’s and maternity hospitals, were not satisfactory. Based on the results of empirical analysis, the four attenuation coefficient models are valid in Shanghai, and the log-logistic model of the inpatient distributions at most hospitals have good simulation effects. However, further in-depth analysis combined with the characteristics of specific specialties is needed to obtain the inpatient model in line with the characteristics of these specialties.

Analysis of the Characteristics of Cross-Regional Patient Groups and Differences in Hospital Service Utilization in Beijing

“…TCM hospitals only treated 2.77% of cross-regional patients from different places and had the largest AR, at 743.53 km. This finding was inconsistent with the results of previous studies concerning the flow of patients among Shanghai hospitals, which indicated that the number of patients in general hospitals declined faster with distance than that of specialist hospitals [ 16 ]. This inconsistency may be related to the study subjects and the scope of patient mobility (the difference between the Jiangsu, Zhejiang, and Shanghai patients treated in the Shanghai hospitals and the national patients treated in Beijing hospitals).…”

“…Existing research has obviously been insufficient with respect to analyzing patients’ cross-regional mobility and empirical research based on cross-regional patient mobility data [ 15 ]. With the rapid development of spatial technology methods, distance decay analysis has gradually become an ideal method of expressing the actual service utilization efficiency of medical resources, which has been reflected by certain related studies concerning the analysis and prediction of patient mobility [ 16 ]. By analyzing the influence of distance factors on patient mobility [ 16 ], it is possible to explore the spatial distribution of the characteristics of cross-regional patient groups and gradually develop a new method of exploring the spatial decay mode of actual hospital utilization in different application scenarios [ 17 ].…”

“…With the rapid development of spatial technology methods, distance decay analysis has gradually become an ideal method of expressing the actual service utilization efficiency of medical resources, which has been reflected by certain related studies concerning the analysis and prediction of patient mobility [ 16 ]. By analyzing the influence of distance factors on patient mobility [ 16 ], it is possible to explore the spatial distribution of the characteristics of cross-regional patient groups and gradually develop a new method of exploring the spatial decay mode of actual hospital utilization in different application scenarios [ 17 ]. Several recent studies have conducted empirical studies, capturing the functional area of cancer care by computing distance decay functions for health care utilization [ 14 ], and found differences in medical distance decay behavior among patient groups of different ages, genders, races, and health insurance statuses [ 18 ].…”

The ecology of medical care in Shanghai

“…Several reports assessed the progress of Shanghai’s medical care systems, in the wake of rapid economic growth, significantly increased life expectancy, massively decreased infant mortality rate, greatly improved accessibility of medical resources [ 15 ]. However some reports also indicated that there are several undesirable features in Shanghai’s health outcome, such as waste of medical resources, inequities in access to health care [ 16 ], different health status across regions of different socioeconomic standings [ 17 ]. However, there is no research describing the framework of utilization of medical resources in Shanghai.…”